In reading order.
Between late 2025 and early 2026 coding agents stopped pausing to ask questions - this series opener maps the five-discipline stack I now use to direct them, from table-stakes prompting to specification engineering.
The prompt is the smallest and least durable artefact in the whole agent system - why the original skill of the AI era stopped being the differentiator, and what good prompt craft still buys you every single day.
Everything that looks like memory in an agent is tokens being written back into the window - the mental model beneath all of context engineering, and the week in June 2025 when the discipline got its name.
LangChain's four-pillar taxonomy gives context engineering its working vocabulary - this part walks the first two pillars in depth, authoring what the agent wakes up knowing and choosing what a task actually needs.
Write and select decide what enters the context window - compress and isolate decide what survives inside it, from intentional compaction to sub-agents and the file system as the agent's real memory.
Context windows degrade before they fill - the evidence for context rot, one team's 40% working rule, and the three ways a window turns toxic long before it runs out of room.
The workflow that turns one long degrading session into three small clean ones - what each phase produces, why the artefacts matter more than the phases, and how much ceremony a task actually deserves.
The permanent infrastructure that makes every agent session start smart - tool configuration, codebase onboarding and workflow automation, and the evidence that the harness moves results more than the model does.
Why the context file that says what an agent must and must not do stays current for months, while the one that describes how the code works is stale by Friday - and how to decide what loads always versus what gets fetched on demand.
Progressive disclosure at repository scale - how a hundred-line map steers agents through a million lines of code, what OpenAI's Codex experiment actually reported, and the benchmark run where changing only the harness moved an agent from Top 30 to Top 5.
An agent can have perfect context and a flawless harness and still optimise for the wrong thing - what Klarna's 2024 launch figures and 2025 reversal actually teach about encoding organisational intent, and three mechanisms that stop that intent drifting.
The highest-leverage discipline in the stack is writing down what done looks like - what a specification must contain before an agent can execute for hours against it, and what Addy Osmani's 80% problem actually says about the work that remains.
The disciplines tell you what to learn - the primitives are what you actually write. Five small, recurring artefacts of AI-directed work, from the self-contained problem statement to the evaluation harness, each shown with a worked example.
Dan Shapiro's Five Levels ladder maps where the industry actually stands with autonomous coding agents - most of it lower than it believes - and StrongDM's three-person software factory shows what the top rung looks like when someone builds it.
The honest retrospective this series owes its readers - the thesis I believe but cannot cite, the threshold that is really one team's habit, the discipline with no how-to, the figures that will age, and the work you should not hand to an agent at all.
